Triple
T1797310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiangyang |
E39633
|
entity |
| Predicate | hasCityWallCondition |
P29117
|
FINISHED |
| Object | well-preserved |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: well-preserved | Statement: [Xiangyang, hasCityWallCondition, well-preserved]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCityWallCondition Context triple: [Xiangyang, hasCityWallCondition, well-preserved]
-
A.
isFortifiedCity
Indicates that a city is strengthened with defensive structures or fortifications, such as walls, ramparts, or similar protective works.
-
B.
hasHistoricTownWallsRemnants
chosen
Indicates that remnants of historic town walls are present and associated with the subject entity.
-
C.
hasWallType
Indicates the specific kind or classification of wall associated with an entity.
-
D.
hasPeaceWalls
Indicates that there exist physical barriers or walls separating groups or areas to reduce or prevent conflict or violence between them.
-
E.
containsFortress
Indicates that a location or area includes a fortress within its boundaries.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a88632aa588190ba3978fde0db5bbd |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab61b6ea188190aab9fb839bf1e367 |
completed | March 6, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69aa61d2f7a8819090301f92d3e358c7 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.